EH-FedSAG: Variance-Reduced Federated Learning with Energy-Aware Participation in Energy-Harvesting IoT

Mateen Ashraf; Onel L.A. Lopez; Richard Demo Souza; Shahab Jahanbazi

REVIEW 2 major objections 1 minor 15 references

EH-FedSAG: Variance-Reduced Federated Learning with Energy-Aware Participation in Energy-Harvesting IoT

T0 review · 2 major / 1 minor · reviewed 2026-06-26 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read EH-FedSAG achieves higher test accuracy and lower variance than EH-FedAvg in energy-harvesting IoT federated learning.

desk verdict EH-FedSAG adds server-memory variance reduction to EH-FedAvg and reports higher accuracy plus lower variance in their EH-IoT simulations, with the edge clearest under low energy or non-IID data. read the letter →

arxiv 2606.18758 v1 pith:5BCEQQVS submitted 2026-06-17 eess.SP

Shahab Jahanbazi , Mateen Ashraf , Richard Demo Souza , Onel L.A. Lopez This is my paper

classification eess.SP

keywords federatedlearningenergyharvestingIoTvariancereductionEH-FedSAGnon-IIDdeviceparticipation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper introduces EH-FedSAG as a variance-reduced approach to federated learning suited for devices that harvest energy from their surroundings. The method uses server memory to stabilize training when device participation is intermittent due to energy constraints. A reader would care because it demonstrates improved performance over standard methods especially when energy is limited and data is not uniformly distributed. The comparison is done within a simulation that accounts for energy arrival, computation, and transmission costs.

What carries the argument

EH-FedSAG, the server-memory-based variance-reduced federated learning method with energy-aware participation.

What would settle it

Deploying EH-FedSAG and EH-FedAvg on physical energy-harvesting IoT hardware and measuring if the accuracy and variance differences persist.

Watch

Extended reading notes

Core claim

The central claim is that EH-FedSAG, a server-memory-based variance-reduced method, consistently achieves higher test accuracy than EH-FedAvg while exhibiting substantially lower training variance. This holds for both homogeneous and heterogeneous data distributions, with the advantage being more pronounced under scarce energy availability and non-IID data.

Load-bearing premise

The unified simulation framework that captures battery charging, local computation cost, and transmission cost under different energy-arrival probabilities accurately represents real energy-harvesting IoT devices.

Editorial extensions

If this is right

EH-FedSAG achieves higher test accuracy than EH-FedAvg.
EH-FedSAG exhibits substantially lower training variance than EH-FedAvg.
The performance advantage of EH-FedSAG is more pronounced under scarce energy availability.
The performance advantage of EH-FedSAG is more pronounced under non-IID data distributions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Implementing EH-FedSAG could enable federated learning in environments where devices rely solely on harvested energy without batteries.
Further work might explore adapting the method to different energy harvesting models or network topologies.
The lower variance could reduce the number of training rounds needed to reach a target accuracy in energy-constrained settings.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

EH-FedSAG is a server-memory variance-reduced version of EH-FedAvg tailored to slot-based energy harvesting. In the simulations it reaches higher test accuracy and cuts training variance compared with the baseline, and the gap widens when energy arrivals are scarce or the data are non-IID.

The work is new in the sense that it ports a memory-based variance reduction step into the EH-FL setting and tests it inside one unified model that includes battery charging, local compute cost, transmission cost, and a multi-channel orthogonal uplink. That framework lets them run controlled comparisons across energy probabilities and data distributions, which is a reasonable way to isolate the effect of the variance reduction.

The results are presented as direct simulation outcomes rather than theoretical guarantees, and the paper does not claim the gains will hold outside the modeled conditions. The main limitation is that the abstract supplies almost no implementation detail on how the server memory is updated or how the variance reduction is applied at each round. There are also no error bars, no mention of statistical tests, and the comparison is only against EH-FedAvg rather than against other FL baselines. Whether the battery and cost model matches real EH hardware remains an open question, but the paper treats the simulation as the evaluation setting and does not overstate it.

The paper is aimed at researchers who already work on federated learning for energy-constrained IoT or sustainable edge systems. Someone looking for a targeted empirical tweak to handle intermittent participation would find the comparison useful. It is not a broad methodological advance, but the concrete setup and the reported difference are clear enough to merit referee time.

Referee Report

2 major / 1 minor

Summary. The paper proposes EH-FedSAG, a server-memory-based variance-reduced federated learning method for energy-harvesting IoT networks. It compares this method to EH-FedAvg within a unified simulation framework that models battery charging, local computation and transmission costs, and multi-channel orthogonal multiple-access uplink under varying energy-arrival probabilities. The results claim that EH-FedSAG achieves higher test accuracy with lower training variance than EH-FedAvg, with the advantage being more pronounced under scarce energy and non-IID data distributions.

Significance. If the simulation results are reliable, this work provides evidence that variance reduction techniques can mitigate the effects of unstable device participation in federated learning for energy-harvesting networks. This could have practical significance for designing efficient FL protocols in IoT systems with intermittent energy sources. The empirical nature without theoretical bounds or hardware experiments limits the immediate impact.

major comments (2)

[Simulation Setup and Results] The performance comparison lacks error bars, statistical significance tests, and details on the number of simulation runs or dataset sizes, making it difficult to assess whether the reported accuracy gains are statistically meaningful or consistent across runs.
[Method Description] The specific implementation of the variance reduction in EH-FedSAG, including how the server memory is used and the update rule, is not sufficiently detailed to allow reproduction or verification of the claimed variance reduction effect.

minor comments (1)

[Abstract] The abstract could benefit from a brief mention of the specific datasets or models used in the simulations for better context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address the two major comments point by point below and will revise the manuscript to improve clarity and reproducibility.

read point-by-point responses

Referee: [Simulation Setup and Results] The performance comparison lacks error bars, statistical significance tests, and details on the number of simulation runs or dataset sizes, making it difficult to assess whether the reported accuracy gains are statistically meaningful or consistent across runs.

Authors: We agree that the current presentation would benefit from these additions. In the revised manuscript we will report results averaged over multiple independent runs with error bars (standard deviation), explicitly state the number of runs and the dataset sizes employed, and include statistical significance tests comparing EH-FedSAG and EH-FedAvg. revision: yes
Referee: [Method Description] The specific implementation of the variance reduction in EH-FedSAG, including how the server memory is used and the update rule, is not sufficiently detailed to allow reproduction or verification of the claimed variance reduction effect.

Authors: We will expand the algorithmic description in the revised version. We will add explicit pseudocode showing the server-memory update, the precise variance-reduced local and global update rules, and a step-by-step explanation of how the memory is maintained and applied under the energy-harvesting participation model. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical simulation result with no load-bearing derivation or self-citation chain

full rationale

The paper proposes EH-FedSAG as a variance-reduced FL method and reports its performance via controlled simulations against EH-FedAvg under a unified energy-harvesting model. No equations, fitted parameters, or theoretical derivations are presented that reduce to their own inputs by construction. The central claim is an empirical observation strictly within the described simulation framework; no uniqueness theorems, ansatzes, or self-citations are invoked as load-bearing support for the accuracy or variance results. This is a standard self-contained empirical comparison.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Only the abstract is available; no equations, parameters, or modeling assumptions are stated, so the ledger cannot be populated with specific entries.

how reviews work

0 comments

Cite this review

Pith. "Pith review of EH-FedSAG: Variance-Reduced Federated Learning with Energy-Aware Participation in Energy-Harvesting IoT." pith.science (2026). https://pith.science/paper/5BCEQQVS

@misc{pith2026260618758,
  author       = {Pith},
  title        = {Pith review of: EH-FedSAG: Variance-Reduced Federated Learning with Energy-Aware Participation in Energy-Harvesting IoT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5BCEQQVS}},
  note         = {Machine review of arXiv:2606.18758}
}

read the original abstract

Federated learning (FL) in energy-harvesting (EH) networks is challenged by intermittent and stochastic energy arrivals that lead to unstable device participation across training rounds, and by high communication costs under limited energy budgets, reducing overall training efficiency. This paper studies FL under a slot-based EH model and proposes EH-FedSAG, a server-memory-based variance-reduced method. We compare EH-FedSAG with vanilla EH-FedAvg under the same multi-channel orthogonal multiple-access uplink model and within a unified simulation framework that captures battery charging, local computation cost, and transmission cost under different energy-arrival probabilities. Performance is assessed in terms of test accuracy over training rounds for both homogeneous and heterogeneous data distributions. The results show that EH-FedSAG consistently achieves higher test accuracy than EH-FedAvg in the considered settings, while exhibiting substantially lower training variance. The advantage of EH-FedSAG is more pronounced under scarce energy availability and non-independent/identically-distributed data.

Figures

Figures reproduced from arXiv: 2606.18758 by the authors.

**Figure 2.** Test accuracy versus communication rounds under different [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Test accuracy versus communication rounds associated with different [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

15 extracted references · 5 canonical work pages

[1]

Communication-Efficient Learning of Deep Networks from Decentralized Data

H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” 2023. [Online]. Available: https://arxiv.org/abs/1602.05629

work page Pith review arXiv 2023
[2]

FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization,

A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani, “FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization,” inProceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research, S. Chiappa and R. Calandra, Eds....

2020
[3]

On the convergence of FedAvg on Non-IID data,

X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of FedAvg on Non-IID data,” inInternational Conference on Learning Representations, 2020. [Online]. Available: https://openreview.net/forum?id=HJxNAnVtDS

2020
[4]

Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint,

Q. Yang, X. Du, X. Wu, W. Wang, A. Liu, and S. Wang, “Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint,”Cybersecurity, 2024. [Online]. Available: https://doi.org/10.1186/s42400-024-00293-x

work page doi:10.1186/s42400-024-00293-x 2024
[5]

A comprehensive survey on client selection strategies in federated learning,

J. Li, T. Chen, and S. Teng, “A comprehensive survey on client selection strategies in federated learning,”Computer Networks, vol. 251, p. 110663, 2024. [Online]. Available: https://www.sciencedirect. com/science/article/pii/S138912862400495X

2024
[6]

A systematic literature review on client selection in federated learning,

C. Smestad and J. Li, “A systematic literature review on client selection in federated learning,” inProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, ser. EASE ’23. New York, NY , USA: Association for Computing Machinery, 2023, p. 2–11. [Online]. Available: https://doi.org/10.1145/ 3593434.3593438

work page arXiv 2023
[7]

FedV ARP: Tackling the variance due to partial client participation in federated learning,

D. Jhunjhunwala, P. Sharma, A. Nagarkatti, and G. Joshi, “FedV ARP: Tackling the variance due to partial client participation in federated learning,” inThe 38th Conference on Uncertainty in Artificial Intelligence, 2022. [Online]. Available: https://openreview.net/forum? id=HlWLLdUocx5

2022
[8]

SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives,

A. Defazio, F. Bach, and S. Lacoste-Julien, “SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives,” inAdvances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper files/paper/ 2014/file/937964195d6fb3a55cd7cc578165f058-Paper.pdf

work page arXiv 2014

Show all 15 references

[9]

Energy-aware federated learning with distributed user sampling and multichannel ALOHA,

R. Valente da Silva, O. L. A. L ´opez, and R. D. Souza, “Energy-aware federated learning with distributed user sampling and multichannel ALOHA,”IEEE Communications Letters, vol. 27, no. 10, pp. 2867– 2871, 2023

2023
[10]

On federated learning with energy harvesting clients,

C. Shen, J. Yang, and J. Xu, “On federated learning with energy harvesting clients,” 2022. [Online]. Available: https://arxiv.org/abs/2202. 06105

2022
[11]

Battery-aware cyclic scheduling in energy- harvesting federated learning,

E. Jeong and N. Pappas, “Battery-aware cyclic scheduling in energy- harvesting federated learning,” in2025 IEEE 26th International Work- shop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC), 2025, pp. 1–5

2025
[12]

Fedseq: A hybrid federated learning framework based on sequential in-cluster training,

Z. Chen, D. Li, R. Ni, J. Zhu, and S. Zhang, “Fedseq: A hybrid federated learning framework based on sequential in-cluster training,” IEEE Systems Journal, vol. 17, no. 3, pp. 4038–4049, 2023

2023
[13]

Federated learning over energy harvesting wireless networks,

R. Hamdi, M. Chen, A. B. Said, M. Qaraqe, and H. V . Poor, “Federated learning over energy harvesting wireless networks,”IEEE Internet of Things Journal, vol. 9, no. 1, p. 92–103, Jan. 2022. [Online]. Available: http://dx.doi.org/10.1109/JIOT.2021.3089054

2022 doi
[14]

Energy harvesting sensor nodes: Survey and implications,

S. Sudevalayam and P. Kulkarni, “Energy harvesting sensor nodes: Survey and implications,”IEEE Communications Surveys & Tutorials, vol. 13, no. 3, pp. 443–461, 2011

2011
[15]

Accounting for variance in machine learning benchmarks,

X. Bouthillier and et al, “Accounting for variance in machine learning benchmarks,” inProceedings of Machine Learning and Systems, A. Smola, A. Dimakis, and I. Stoica, Eds., vol. 3, 2021, pp. 747–769. [Online]. Available: https://proceedings.mlsys.org/paper files/ paper/2021/f...

2021

Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.

[1] [1]

Communication-Efficient Learning of Deep Networks from Decentralized Data

H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” 2023. [Online]. Available: https://arxiv.org/abs/1602.05629

work page Pith review arXiv 2023

[2] [2]

FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization,

A. Reisizadeh, A. Mokhtari, H. Hassani, A. Jadbabaie, and R. Pedarsani, “FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization,” inProceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, ser. Proceedings of Machine Learning Research, S. Chiappa and R. Calandra, Eds....

2020

[3] [3]

On the convergence of FedAvg on Non-IID data,

X. Li, K. Huang, W. Yang, S. Wang, and Z. Zhang, “On the convergence of FedAvg on Non-IID data,” inInternational Conference on Learning Representations, 2020. [Online]. Available: https://openreview.net/forum?id=HJxNAnVtDS

2020

[4] [4]

Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint,

Q. Yang, X. Du, X. Wu, W. Wang, A. Liu, and S. Wang, “Fedrtid: an efficient shuffle federated learning via random participation and adaptive time constraint,”Cybersecurity, 2024. [Online]. Available: https://doi.org/10.1186/s42400-024-00293-x

work page doi:10.1186/s42400-024-00293-x 2024

[5] [5]

A comprehensive survey on client selection strategies in federated learning,

J. Li, T. Chen, and S. Teng, “A comprehensive survey on client selection strategies in federated learning,”Computer Networks, vol. 251, p. 110663, 2024. [Online]. Available: https://www.sciencedirect. com/science/article/pii/S138912862400495X

2024

[6] [6]

A systematic literature review on client selection in federated learning,

C. Smestad and J. Li, “A systematic literature review on client selection in federated learning,” inProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, ser. EASE ’23. New York, NY , USA: Association for Computing Machinery, 2023, p. 2–11. [Online]. Available: https://doi.org/10.1145/ 3593434.3593438

work page arXiv 2023

[7] [7]

FedV ARP: Tackling the variance due to partial client participation in federated learning,

D. Jhunjhunwala, P. Sharma, A. Nagarkatti, and G. Joshi, “FedV ARP: Tackling the variance due to partial client participation in federated learning,” inThe 38th Conference on Uncertainty in Artificial Intelligence, 2022. [Online]. Available: https://openreview.net/forum? id=HlWLLdUocx5

2022

[8] [8]

SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives,

A. Defazio, F. Bach, and S. Lacoste-Julien, “SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives,” inAdvances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper files/paper/ 2014/file/937964195d6fb3a55cd7cc578165f058-Paper.pdf

work page arXiv 2014